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1 June 2020 Aerial 3D building reconstruction from RGB drone imagery
Marc WuDunn, Avideh Zakhor, Samir Touzani, Jessica Granderson
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Proceedings Volume 11398, Geospatial Informatics X; 1139803 (2020) https://doi.org/10.1117/12.2558399
Event: SPIE Defense + Commercial Sensing, 2020, Online Only
Abstract
3D Building Reconstruction is an important problem with applications in urban planning, emergency response, and disaster planning. This paper presents a new pipeline for 3D reconstruction of buildings from RGB imagery captured via a drone. We leverage the commercial software Pix4D to construct a 3D point cloud from RGB drone imagery, which is then used in conjunction with image processing and geometric methods to extract a building footprint. The footprint is then extruded vertically based on the heights of the segmented rooftops. The footprint extraction involves two main steps, line segment detection and polygonization of the lines. To detect line segments, we project the point cloud onto a regular grid, detect preliminary lines using the Hough transform, refine them via RANSAC, and convert them into line segments by checking the density of the points surrounding the line. In the polygonization step, we convert detected line segments into polygons by constructing and completing partial polygons, and then filter them by checking for support in the point cloud. The polygons are then merged based on their respective height profiles. We have tested our system on two buildings of several thousand square feet in Alameda, CA, and obtained an F1 score of 0.93 and 0.95 respectively as compared to the ground truth.
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Marc WuDunn, Avideh Zakhor, Samir Touzani, and Jessica Granderson "Aerial 3D building reconstruction from RGB drone imagery", Proc. SPIE 11398, Geospatial Informatics X, 1139803 (1 June 2020); https://doi.org/10.1117/12.2558399
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KEYWORDS
Image segmentation
Hough transforms
3D image processing
3D modeling
LIDAR
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